Facilitation of passive intermodulation cancelation via machine learning

ABSTRACT

A passive intermodulation detection system is provided to remotely identify passive intermodulation at a base station site and diagnose the type of intermodulation and location of the non-linearity that is the source of the passive intermodulation. A passive intermodulation cancelation system can generate an equivalent signal to a received interference signal and use the equivalent signal to generate an error signal. The error signal can then be used to reinforce a learning system and converge on a steady state of the interference signal to cancel other interference signals.

RELATED APPLICATION

The subject patent application is a continuation of, and claims priorityto, U.S. patent application Ser. No. 15/640,382, filed Jun. 30, 2017,and entitled “FACILITATION OF PASSIVE INTERMODULATION CANCELATION VIAMACHINE LEARNING,” the entirety of which application is herebyincorporated by reference herein.

TECHNICAL FIELD

This disclosure relates generally to facilitating the cancelation ofpassive intermodulation for antennas. More specifically, this disclosurerelates to cancelation of passive intermodulation using machinelearning.

BACKGROUND

Intermodulation is the amplitude modulation of signals containing two ormore different frequencies in a system with non-linearities that resultsin signal noise. The intermodulation between each frequency componentcan form additional signals at frequencies that are harmonic frequenciesand sum and difference frequencies of the original frequencies andmultiples thereof. The non-linearities can be caused by junctions in thephysical equipment (cables, antennas), as well as by sources in thesurrounding environment. This type of intermodulation, caused bynon-active components, is called external (in the sense the passiveintermodulation sources are external to the cabling/antenna system)passive intermodulation and can be difficult and costly to diagnose assite visits by skilled technicians are traditionally used to detect andidentify the non-linearity source locations.

The above-described background relating to intermodulation is merelyintended to provide a contextual overview of some current issues, and isnot intended to be exhaustive. Other contextual information may becomefurther apparent upon review of the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments of the subject disclosureare described with reference to the following figures, wherein likereference numerals refer to like parts throughout the various viewsunless otherwise specified.

FIG. 1 illustrates an example wireless network comprising passiveintermodulation cancelation according to one or more embodiments.

FIG. 2 illustrates an example passive intermodulation cancelation moduleaccording to one or more embodiments.

FIG. 3 illustrates an example passive intermodulation cancelationmodule, wherein the transmitter and receive are external to the moduleaccording to one or more embodiments.

FIG. 4 illustrates an example schematic system block diagram for asystem to perform passive intermodulation cancelation according to oneor more embodiments

FIG. 5 illustrates an example graph for a passive intermodulationcancelation, wherein a galvanized bolt is the passive intermodulationsource.

FIG. 6 illustrates an example graph for a passive intermodulationcancelation, wherein a galvanized bolt is the passive intermodulationsource.

FIG. 7 illustrates an example graph for a passive intermodulationcancelation, wherein a galvanized bolt is the passive intermodulationsource.

FIG. 8 illustrates an example graph for a passive intermodulationcancelation training, wherein a rusty bolt is the passiveintermodulation source.

FIG. 9 illustrates an example schematic system block diagram for amethod to perform passive intermodulation cancelation according to oneor more embodiments.

FIG. 10 illustrates an example schematic system block diagram of anexample non-limiting embodiment of a computing environment in accordancewith various aspects described herein.

FIG. 11 illustrates an example block diagram of an example, non-limitingembodiment of a mobile network platform in accordance with variousaspects described herein.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth toprovide a thorough understanding of various embodiments. One skilled inthe relevant art will recognize, however, that the techniques describedherein can be practiced without one or more of the specific details, orwith other methods, components, materials, etc. In other instances,well-known structures, materials, or operations are not shown ordescribed in detail to avoid obscuring certain aspects.

Reference throughout this specification to “one embodiment,” or “anembodiment,” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment. Thus, the appearances of the phrase “in oneembodiment,” “in one aspect,” or “in an embodiment,” in various placesthroughout this specification are not necessarily all referring to thesame embodiment. Furthermore, the particular features, structures, orcharacteristics may be combined in any suitable manner in one or moreembodiments.

As utilized herein, terms “component,” “system,” “interface,” and thelike are intended to refer to a computer-related entity, hardware,software (e.g., in execution), and/or firmware. For example, a componentcan be a processor, a process running on a processor, an object, anexecutable, a program, a storage device, and/or a computer. By way ofillustration, an application running on a server and the server can be acomponent. One or more components can reside within a process, and acomponent can be localized on one computer and/or distributed betweentwo or more computers.

Further, these components can execute from various machine-readablemedia having various data structures stored thereon. The components cancommunicate via local and/or remote processes such as in accordance witha signal having one or more data packets (e.g., data from one componentinteracting with another component in a local system, distributedsystem, and/or across a network, e.g., the Internet, a local areanetwork, a wide area network, etc. with other systems via the signal).

As another example, a component can be an apparatus with specificfunctionality provided by mechanical parts operated by electric orelectronic circuitry; the electric or electronic circuitry can beoperated by a software application or a firmware application executed byone or more processors; the one or more processors can be internal orexternal to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts; the electroniccomponents can include one or more processors therein to executesoftware and/or firmware that confer(s), at least in part, thefunctionality of the electronic components. In an aspect, a componentcan emulate an electronic component via a virtual machine, e.g., withina cloud computing system.

The words “exemplary” and/or “demonstrative” are used herein to meanserving as an example, instance, or illustration. For the avoidance ofdoubt, the subject matter disclosed herein is not limited by suchexamples. In addition, any aspect or design described herein as“exemplary” and/or “demonstrative” is not necessarily to be construed aspreferred or advantageous over other aspects or designs, nor is it meantto preclude equivalent exemplary structures and techniques known tothose of ordinary skill in the art. Furthermore, to the extent that theterms “includes,” “has,” “contains,” and other similar words are used ineither the detailed description or the claims, such terms are intendedto be inclusive—in a manner similar to the term “comprising” as an opentransition word—without precluding any additional or other elements.

As used herein, the term “infer” or “inference” refers generally to theprocess of reasoning about, or inferring states of, the system,environment, user, and/or intent from a set of observations as capturedvia events and/or data. Captured data and events can include user data,device data, environment data, data from sensors, sensor data,application data, implicit data, explicit data, etc. Inference can beemployed to identify a specific context or action, or can generate aprobability distribution over states of interest based on aconsideration of data and events, for example.

Inference can also refer to techniques employed for composinghigher-level events from a set of events and/or data. Such inferenceresults in the construction of new events or actions from a set ofobserved events and/or stored event data, whether the events arecorrelated in close temporal proximity, and whether the events and datacome from one or several event and data sources. Various classificationschemes and/or systems (e.g., support vector machines, neural networks,expert systems, Bayesian belief networks, fuzzy logic, and data fusionengines) can be employed in connection with performing automatic and/orinferred action in connection with the disclosed subject matter.

In addition, the disclosed subject matter can be implemented as amethod, apparatus, or article of manufacture using standard programmingand/or engineering techniques to produce software, firmware, hardware,or any combination thereof to control a computer to implement thedisclosed subject matter. The term “article of manufacture” as usedherein is intended to encompass a computer program accessible from anycomputer-readable device, computer-readable carrier, orcomputer-readable media. For example, computer-readable media caninclude, but are not limited to, a magnetic storage device, e.g., harddisk; floppy disk; magnetic strip(s); an optical disk (e.g., compactdisk (CD), a digital video disc (DVD), a Blu-ray Disc™ (BD)); a smartcard; a flash memory device (e.g., card, stick, key drive); and/or avirtual device that emulates a storage device and/or any of the abovecomputer-readable media.

As an overview, various embodiments are described herein to facilitatepassive intermodulation cancelation between network devices.

For simplicity of explanation, the methods (or algorithms) are depictedand described as a series of acts. It is to be understood andappreciated that the various embodiments are not limited by the actsillustrated and/or by the order of acts. For example, acts can occur invarious orders and/or concurrently, and with other acts not presented ordescribed herein. Furthermore, not all illustrated acts may be requiredto implement the methods. In addition, the methods could alternativelybe represented as a series of interrelated states via a state diagram orevents. Additionally, the methods described hereafter are capable ofbeing stored on an article of manufacture (e.g., a machine-readablestorage medium) to facilitate transporting and transferring suchmethodologies to computers. The term article of manufacture, as usedherein, is intended to encompass a computer program accessible from anycomputer-readable device, carrier, or media, including a non-transitorymachine-readable storage medium.

It is noted that although various aspects and embodiments are discussedherein with respect to Universal Mobile Telecommunications System (UMTS)and/or Long Term Evolution (LTE), the disclosed aspects are not limitedto a UMTS implementation and/or an LTE implementation. For example,aspects or features of the disclosed embodiments can be exploited insubstantially any wireless communication technology. Such wirelesscommunication technologies can include 5G, UMTS, Code Division MultipleAccess (CDMA), Wi-Fi, Worldwide Interoperability for Microwave Access(WiMAX), General Packet Radio Service (GPRS), Enhanced GPRS, ThirdGeneration Partnership Project (3GPP), LTE, Third Generation PartnershipProject 2 (3GPP2) Ultra Mobile Broadband (UMB), High Speed Packet Access(HSPA), Evolved High Speed Packet Access (HSPA+), High-Speed DownlinkPacket Access (HSDPA), High-Speed Uplink Packet Access (HSUPA), Zigbee,or another IEEE 802.XX technology. Additionally, substantially allaspects disclosed herein can be exploited in legacy telecommunicationtechnologies.

Described herein are systems, methods, articles of manufacture, andother embodiments or implementations that can facilitate passiveintermodulation cancelation. A passive intermodulation detection systemcan remotely identify passive intermodulation at a base station site anddiagnose the type of intermodulation and location of the non-linearitythat is the source of the passive intermodulation. Additionally, thepassive intermodulation detection system can identify wireless carriersassociated with the passive intermodulation. The passive intermodulationdetection system can detect a first transmission signal in a first bandthat is transmitted by an antenna. Another antenna or the same antennacan receive the first transmission signal in another band, and thepassive intermodulation detection system can analyze the received signalto determine whether an intermodulation product due to an external orinternal non-linearity is present. Based on the type of intermodulationproduct, period, order, frequency, etc., the type (internal or externalor both), magnitude, and location of the non-linearity can beidentified. The passive intermodulation detection system can alsodistinguish passive intermodulation noise from adjacent channelinterference.

Once the characteristics of the passive intermodulation are discoveredand analyzed, this information can then be used to effectively cancelthe effect of the nonlinearities by use of the known characteristics ofthe received transmission signals and the estimated characteristics ofthe nonlinearity sources. A cloud based machine learning approach can beused to learn, model, and cancel passive intermodulation via softwarethat can be downloaded from an open network automation process (ONAP) toa software-defined network (SDN) and/or a network functionvirtualization (NFV). Furthermore, the machine learning can be based onnon-parametric learning, requiring limited or no a-priori knowledge ofthe environment and PIM source specifics. The use of machine learningcan allow the passive intermodulation cancelation system to adapt sovarious use cases involving various carriers with various transmissionand reception port signals, which may be susceptible to varying levelsof interference. Because various carriers can have varying levels ofpassive intermodulation, it is important to identify the strongestpassive intermodulation and cancel it. The use of machine learningtechniques can learn in near real time implement cancelation in aflexible architecture to deal with different types of passiveintermodulation. The learning solution can also be extended to includeantenna beamforming techniques to jointly cancel and reject externalpassive intermodulation signals.

A network core with an SDN controller can control routing of trafficwithin the network and cancelation of passive intermodulation signalsbetween the network and the traffic destination. The SDN controller canbe merged with existing 3^(rd) Generation Partnership Project (“3GPP”)network architecture to enable service deliveries via open applicationprogramming interfaces (“APIs”) and move the network core towards an allinternet protocol (“IP”), cloud based, and software driven telecomnetwork. The SDN controller can work with, or take the place of policyand charging rules function (“PCRF”) network elements so that policiessuch as quality of service (“QoS”) and traffic management and routingcan be synchronized and managed end to end.

An LTE network is a policy-based traffic management architecture with aPCRF element traditionally controlling the QoS levels and otherinformation (priorities bandwidths, etc.) that manages IP flows thatcarries a particular application (such as voice, video, messaging,etc.). This policy-based mechanism applies to the IP traffic between themobile device and the packet data network gateway (“PGW”). In anembodiment of the subject disclosure, software defined networking can beused to cancel passive intermodulation signals from transceiver ports toreceiver ports. In some embodiments, the SDN controller can also providetraffic control for packets from the mobile device to the destinationbased on a severity of the severity of the signal interference.

To cancel a reflected signal, the non-linearity, the multipath to thepassive intermodulation (PIM) source, the reflected signal multipath,and the reception filtering can be modeled with machine learningtechniques. A downlink from two different bands can transmit twopolarizations each (F1 at +/−45°) and (F2 at +/−45°), which can generatefour products. Each product can be analyzed separately to determinewhich product dominates in order to simplify the cancelation and theconversion from a non-linear system into a linear system. When there isa single band, the analysis can determine an internal PIM, but theanalysis can also determine an external PIM for the single band byisolating the band to determine if there is any intermodulation on theband. Respectively, a dual antenna system with adequate isolation candetermine intermodulation for two bands.

Generally, a third order product from a PIM non-linearity is thestrongest. Therefore, canceling the third order product can be thepriority. However, it should be noted that any other order couldpotentially be stronger than the third order product. Therefore, anyother order can be selected for cancelation. The products can comprisetransmission signals and complex amplitude and phase adjustments,wherein the transmissions ports can generate multiple frequency bands.For example, a PIM source can be modeled to the third order and give thefollowing in-band products for a band 17 uplink:

$\begin{matrix}{\left( {{Ka} \times 2\; 9H} \right)^{3} + \left( {{Kd} \times 29V} \right)^{3} + \left\lbrack {\left( {{Kc} \times 29H} \right) \times \left( {{Kd} \times 29V} \right)} \right\rbrack^{3} + \left\lbrack {\left( {{Ka} \times 17H} \right)\left( {{Kc} \times 29H} \right)} \right\rbrack^{2} + {\left( {{Ka} \times 17V} \right) \times \left( {{Kd} \times 29V} \right)^{2}} + {\left( {{Ka} \times 17H} \right) \times \left( {{Kd} \times 29V} \right)^{2}} + {\left( {{Kb} \times 17V} \right) \times \left( {{Kc} \times 29H} \right)^{2}}} & {{Eqn}\mspace{14mu} (1)}\end{matrix}$

The band 17 self-products can be ignored since they are far away infrequency (but for completeness, they can appear exactly like the band29 above except 29 is replaced by 17).

The PIM can be modeled to determine the largest intermodulation thatimpacts the band 17 uplink by placing the third order intermodulationproducts into the desired signal. The average magnitude of the thirdorder intermodulation product components can be calculated and rankedbased on the signal plus interference noise ratio/physical resourceblock bandwidth. Thereafter, the two highest values can be input intothe canceler system. However, it should be noted that one PIM or severalPIMs can be canceled simultaneously.

Due to the enormity and complexity of the unknowns associated withpassive intermodulation, it is difficult to use a parameterized model ofthe known transmission signals (through propagation and polarizationrotation) to construct a reception model of the intermodulation signals,which can be used to cancel the received intermodulation interferencesignals. Therefore, a non-parametric learning model can identify thevarious transmission signals that cause significant interference and usethem to cancel the received intermodulation interference.

Passive intermodulation identification techniques can be used toidentify interference signals that are interference with receivedtransmission. These identification techniques can identify carriers andrespective antenna ports that contribute to the passive intermodulationat each reception port. As noted above, there are products associatedwith each of the signals experiencing the transmission interference. Theproducts can be ranked, by order of the severity of the signalinterference, to determine which signal interference should be canceledfirst. Once the signal experiencing the greatest interference isdetermined via the ranking of its products, a conforming signal can begenerated by the system. It should be noted that the conforming signalcan be equivalent to, match, be a duplicate of, and/or be similar to thesignal experiencing the greatest interference. Additionally, it shouldbe noted that carriers and/or antenna ports can be loaded at 100%utilization of their respective physical resource blocks. The passiveintermodulation cancelation system can then generate atransmission/reception channel that can be used to estimate the receivedintermodulation interference based on another transmission signal. Theestimated signal can then be subtracted from the received signal toyield an error signal. The error signal can then be used as an output tocancel future passive intermodulation signals.

The error output can be placed through the machine learning process toconverge on a steady state of the interfering signal. The cancelationsystem can also account for any time delay due to propagation varianceassociated with time. In conjunction with the machine learning andcancelation system, the model can also comprise a pre-filter to modelthe known properties of any uplink filter to avoid additional adaptivefilter steps associated with a learning filter that can increasecomplexity and slow convergence. The pre-filter can also model thereception antenna and filter. Additionally, the machine learningtechniques can be implemented as a cloud-based radio access networksoftware module controlled by the ONAP, which can route any transmissionport signal(s) to the passive intermodulation cancelation system for agive reception port(s).

To achieve a minimum cancelation objective with the lowest complexityconfiguration, the passive intermodulation cancelation system canperform these operations during a maintenance window. The maintenancewindow is a period of reduced or low wireless network usage (e.g., aftermidnight) that takes a network element out of service or partially outof service, where the network element can be altered. The maintenancewindow can also be determined based on maintenance windows of otherneighboring sites. For example, a good maintenance window for one sitecan be when the there are no maintenance windows for its neighboringsites so that the neighboring sites can pick up the additional networkload. Consequently, the maintenance window can be used to train thepassive intermodulation cancelation system. The data developed duringthe training, can then be used in real-time live period, outside of themaintenance window, to achieve passive intermodulation cancelation inreal-time.

In one embodiment, described herein is a method comprising identifying,by a network device, comprising a processor, of a wireless network, atransmission port associated with a signal interference of a receptionport, and determining, by network device, an interference productassociated with the signal interference. Based on the determining themethod can rank, by the network device, the interference productaccording to a magnitude of the signal interference, resulting in afirst ranked interference signal. Consequently, based on a conditionassociated with the first ranked interference signal being determined tohave been satisfied, the method can comprise generating, by the networkdevice, a second ranked interference signal that conforms to the firstranked interference signal.

For these considerations as well as other considerations, in one or moreembodiments, a system comprises a processor and a memory that storesexecutable instructions that when executed by the processor, facilitateperformance of operations, comprising determining a first signalinterference associated with a first signal received by a receiverdevice, and determining an interference product as a function of thefirst signal interference. Based on the interference product, theoperations can comprise generating rank data representative of a rank ofthe first signal interference, and applying the rank data to the firstsignal interference to generate a ranked interference signal.Furthermore, based on the rank data, the operations can comprisegenerating a second signal interference, and utilizing a channelassociated with a transmitter device and the receiver device tofacilitate modification of a second signal to be sent from thetransmission device to the receiver device.

According to yet another embodiment, described herein is amachine-readable storage medium that can perform the operationscomprising receiving first signal data related to a transmission signalfrom a transmission port device to a reception port device, wherein asignal interference associated with the transmission signal isdetermined to be present at the reception port device. Based ondetermining an interference product associated with the signalinterference, the operations can comprise ranking the interferenceproduct, and base on a result of ranking the signal interference inaccordance with the ranking of the interference product, generating aranked interference signal.

These and other embodiments or implementations are described in moredetail below with reference to the drawings.

Referring now to FIG. 1, illustrated is an example wireless networkcomprising passive intermodulation cancelation according to one or moreembodiments. A passive intermodulation source 100 can reflecttransmission signals 106, 108 from reception antennas 102, 104. Forexample, reflected transmission signal 110 can be reflected to theantenna 102 in response to the passive intermodulation cancelationsource receiving the transmission signals 106, 108. Additionally, itshould be noted that various bands can be associated with thetransmission signals 106, 108. For example, the reception antenna 102can be associated with band 17 thereby associating band 17 with thetransmission signal 106, and the reception antenna 104 can be associatedwith band 29 thereby associating band 29 with the transmission signal108. To cancel the reflected transmission signal 110, the non-linearityof the passive intermodulation source 100, the transmission signals 106,108 and reception signals, can be modeled with an adaptive passiveintermodulation cancelation module. Additionally, in an alternateembodiment, the transmission signals 106, 108 and reception signals canbe external to the passive intermodulation cancelation module.

Referring now to FIG. 2, illustrated is an example passiveintermodulation cancelation module according to one or more embodiments.Repetitive description of like elements employed in other embodimentsdescribed herein is omitted for sake of brevity.

A base station site (e.g., a cell tower or other location where a basestation device can be located) can include one or more remote radioheads (RRH) that can send transmissions to one or more mobile devicesthat are located within range of the base station site. Non-linearitiesin passive elements (e.g., antennas, cabling, junctions betweenmaterials, etc) can cause passive intermodulation when two or more highpower tones mix at the nonlinearities (e.g., junctions of dissimilarmetals, rust, and even loose connectors). In the embodiment shown inFIG. 2, the passive intermodulation cancelation module 200 can comprisea transmitter component 202 that can transmit wireless data to areceiver component 204. The passive intermodulation cancelation module200 can also comprise an analysis component 206, an intermodulationdetection component 208, and a cancelation component 210. It should benoted that any of the aforementioned components or their sub-components,processor 212, and memory 314 can bi-directionally communicate with eachother. It should also be noted that in alternative embodiments thatother components including, but not limited to the sub-components,processor 212, and/or memory 314, can be external to the passiveintermodulation cancelation module 200, as shown in FIG. 2.

The passive intermodulation cancelation module 200 can be configured todetect passive intermodulation caused by nonlinearities at or externalto a cell site. The transmitter component 202 can transmit a signal viaan antenna. Such transmissions can comprise a plurality of frequenciesduring normal operations. For instance, the transmission signal can bein a first band. The receiver component 204 can receive, via anotherantenna, the transmission in a second band. For instance, the signal canbe in a downlink band when transmitted, while signals received by thereceiver component 204 can be from a mobile device in an uplink band.

The intermodulation detection component 208 can detect whether thesignal, as received by the receiver component 204 includes anyintermodulation products from passive intermodulation. In an embodiment,the intermodulation detection component 208 can determine that anintermodulation product from the transmission signal is present in thetransmission. The intermodulation detection component 208 can alsodistinguish the intermodulation product from adjacent channelinterference associated with a signal on an adjacent channel based onthe slope of the noise amplitude as a function of frequency. The furtherthe frequency is from the adjacent band, the more the noise amplitudedecreases. By contrast, the intermodulation product from the passiveintermodulation has harmonics that show up as increases at regularfrequency intervals. Additionally, the intermodulation detectioncomponent 208 can identify the carriers associated with antennas thatcontribute to the intermodulation interference.

The analysis component 206 can determine a type of a source ofnon-linearity based on an amplitude and a period of the intermodulationproduct. This can also determine characteristics of the nonlinearity foruse in possible cancelation. The analysis component 206 can alsodetermine a location of the source of non-linearity based on a timedelay between the intermodulation product and the transmission signal.Furthermore, the analysis component 206 can also rank intermodulationproducts (based on a severity of the interference) created by thenon-linearity that is predictive of intermodulation products indifferent contexts (band, frequency, amplitude, etc.). Once the analysiscomponent 206 has ranked the interference products, the analysiscomponent 206 can send data to the cancelation component 210 to generateanother signal to match the signal associated with the highest rankedinterference product. The other signal can then be used by thecancelation component 210 to modify or otherwise process transmissionsto mitigate the intermodulation product on transmissions received byreceiver component 204.

The intermodulation detection component 208 and the analysis component206 can send their outputs to the cancelation component 210 to cancelreflected signals. The cancelation component 210 can account forinterference associated with a signal that might have been delayed byhitting an object (i.e. a bolt). However, the interference could alsohave a frequency and/or a time offset. Additionally, the cancelationcomponent 210 can generate a transmission reception channel that cantransform additional transmission signals to an estimated interferencesignal received by the receiver component 204. The estimatedinterference can then be subtracted from the interference signal(associated with the highest ranked interference product) to produce anerror signal. Therefore an error output, based on the error signal, canbe used to adaptively modify future estimates to account for time delay.

Aspects of the processor 212 can constitute machine-executablecomponent(s) embodied within machine(s), e.g., embodied in one or morecomputer readable mediums (or media) associated with one or moremachines. Such component(s), when executed by the one or more machines,e.g., computer(s), computing device(s), virtual machine(s), etc. cancause the machine(s) to perform the operations described by the passiveintermodulation cancelation module 200. In an aspect, the passiveintermodulation cancelation module 200 can also include memory 214 thatstores computer executable components and instructions.

Referring now to FIG. 3, illustrated is an example passiveintermodulation cancelation module system 300, wherein the transmitterand receiver are external to the module according to one or moreembodiments. A passive intermodulation cancelation module 200 cancomprise an analysis component 206, an intermodulation detectioncomponent 208, and a cancelation component 210. In this particularembodiment, the transmitter component 202 and the receiver component 204can be external to the passive intermodulation cancelation component200. However, it should be noted that in alternate embodiments, thetransmitter component 202 and the receiver component 204 can be internalto the intermodulation cancelation component 200, as shown in FIG. 2. Itshould be noted that any of the aforementioned components or theirsub-components (e.g., subtraction component 302, signal estimationcomponent 304, signal modification component 306, signal generationcomponent 308, and channel generation component 310), processor 212, andmemory 314 can bi-directionally communicate with each other. It shouldalso be noted that in alternative embodiments that other componentsincluding, but not limited to the sub-components, processor 212, and/ormemory 314, can be external to the passive intermodulation component200.

The passive intermodulation cancelation component 200 can receive dataassociated with a transmission signal from an antenna of the transmittercomponent 202 to an antenna of the receiver component 204. Thetransmission signal can be in a downlink band, while the signal receivedby the receiver component 204 can be in an uplink band.

A base station site (e.g., a cell tower or other location where a basestation device can be located) can include one or more RRH that can sendtransmissions to one or more mobile devices that are located withinrange of the base station site. Non-linearities in passive elements(e.g., antennas, cabling, junctions between materials, etc) can causepassive intermodulation when two or more high power tones mix at thenonlinearities (e.g., junctions of dissimilar metals, rust, and evenloose connectors). Therefore, the passive intermodulation cancelationcomponent 200 can be used to cancel the nonlinearities associated withsuch intermodulation.

The intermodulation detection component 208 can detect whether thesignal, as received by the receiver component 204, includes anyintermodulation products from passive intermodulation. Theintermodulation detection component 208 can determine that anintermodulation product from the transmission signal is present in thetransmission. The intermodulation detection component 208 can alsodistinguish the intermodulation product from adjacent channelinterference associated with a signal on an adjacent channel based onthe slope of the noise amplitude as a function of frequency. The furtherthe frequency is from the adjacent band, the more the noise amplitudedecreases. By contrast, the intermodulation product from the passiveintermodulation has harmonics that show up as increases at regularfrequency intervals. Additionally, the intermodulation detectioncomponent 208 can identify the carriers associated with antennas thatcontribute to the intermodulation interference.

The analysis component 206 can comprise the ranking component 312. Theanalysis component 206 can determine a type of a source of non-linearitybased on an amplitude and a period of the intermodulation product. Thiscan also determine characteristics of the nonlinearity for use inpossible cancelation. The analysis component 206 can also determine alocation of the source of non-linearity based on a time delay betweenthe intermodulation product and the transmission signal. Furthermore,the analysis component 206 can rank, via the ranking component 312,intermodulation products (based on a severity of the interference)created by the non-linearity that is predictive of intermodulationproducts in different contexts (band, frequency, amplitude, etc).

Once the analysis component 206 has ranked the interference products,the analysis component 206 can send rank data to the cancelationcomponent 210 to generate another signal (e.g., via the signalgeneration component 308) to match the signal associated with thehighest ranked interference product. The other signal can then be usedby the cancelation component 210 to modify (e.g., via the signalmodification component 306) or otherwise process transmissions tomitigate the intermodulation product on transmissions received byreceiver component 204.

The intermodulation detection component 208 and the analysis component206 can send their outputs to the cancelation component 210 to cancelreflected signals. The cancelation component 210 can account forinterference associated with a signal that might have been delayed byhitting an object (i.e. a bolt). However, the interference could alsohave a frequency and/or a time offset. Additionally, the cancelationcomponent 210 can generate a transmission reception channel (e.g., viathe channel generation component 310) that can transform additionaltransmission signals to an estimated interference signal (e.g., via thesignal estimation component 304) received by the receiver component 204.The estimated interference can then be subtracted (e.g., via thesubtraction component 302) from the interference signal (associated withthe highest ranked interference product) to produce an error signal.Therefore an error output, based on the error signal, can be used toadaptively modify future estimates to account for time delay of signals.

Aspects of the processor 212 can constitute machine-executablecomponent(s) embodied within machine(s), e.g., embodied in one or morecomputer readable mediums (or media) associated with one or moremachines. Such component(s), when executed by the one or more machines,e.g., computer(s), computing device(s), virtual machine(s), etc. cancause the machine(s) to perform the operations described by the passiveintermodulation cancelation module 200. In an aspect, the passiveintermodulation cancelation module 200 can also include memory 214 thatstores computer executable components and instructions.

Referring now to FIG. 4, illustrated is an example schematic systemblock diagram for a system to perform passive intermodulationcancelation according to one or more embodiments. In another embodiment,the transmitter component 202 can transmit a transmission signal to thereceiver component 204. However, the transmission signal can experiencesignal interference from the passive intermodulation source 100. Thetransmission signal and the signal interference can be received by theRRH 402. The RRH 402 can perform a bidirectional translation of thetransmission and receive radio frequencies in the form of digitalbaseband signals. However, the transmission signal and the signalinterference can also be received by the passive intermodulationcancelation module 200.

The passive intermodulation cancelation module 200 can determine that anintermodulation product from the transmission signal is present in thetransmission. Additionally, the intermodulation detection component 208can identify the carriers associated with antennas that contribute tothe intermodulation interference. Furthermore, the passiveintermodulation cancelation module 200 can also rank (e.g., via theanalysis component 206) intermodulation products (based on a severity ofthe interference) created by the non-linearity that is predictive ofintermodulation products in different contexts (band, frequency,amplitude, etc). Another signal can be generated (e.g., via the signalgeneration component 308) to modify or otherwise process transmissionsto mitigate the intermodulation product on transmissions received byreceiver component 204.

The passive intermodulation cancelation component 200 can also generatea transmission reception channel (e.g., via the channel generationcomponent 310) that can transform additional transmission signals to anestimated interference signal received by the receiver component 204.The estimated interference can then be subtracted (e.g., via thesubtraction component 302 at the BBU 404) from the interference signal(associated with the highest ranked interference product) to produce anerror signal. The passive intermodulation cancelation module 200 cansend cancelation signals to the BBU 404 to process the cancelationsignals along with the baseband. The BBU 404 can subsequentlycommunicate back to the cancelation module 200 the resultant errorsignal, or the error signal can be computed by the cancelation module200 if it has knowledge of the baseband received signal from the RRH402. Either linkage can be represented by the bidirectional arrowbetween the BBU 404 and the cancelation module 200. Therefore an erroroutput, based on the error signal, can be used to adaptively modifyfuture estimates to account for time delay.

Referring now to FIGS. 5-8, illustrated are example graphs for a passiveintermodulation cancelation, wherein a galvanized bolt is the passiveintermodulation source. FIGS. 5-7 depict how a simulated theory versusactual passive intermodulation data compares to a moving average. Thesegraphs illustrate how the instantaneous amplitude of the measured PIM(R×A) compares with a theoretical model based on a time-invariant3rd-order intermodulation resultant (Theory), and a 5-pointmoving-average of the measured data (dotted curve). Amplitude is alinear scale with an arbitrary reference level set appropriate for theanalog-to-digital converter dynamic range. The X-axis is linear time,roughly 30 ns per sample point. Here, a piece of hardware made ofgalvanized steel was used as the PIM source.

It should be noted that although the relative amplitude of thetheoretical curve versus measured PIM is shown, the ultimate action ofthe adaptive filter is to dynamically adjust the gain and phase tominimize the error. The theory curve is based on knowledge of theinstantaneous transmit signals, and an assumed dominance of the 3rdorder mixing product. The true received PIM can be dependent on transmitand receive path effects, reflections from surrounding objects,near-field coupling of field components and PIM object orientation, andthe “true” nature of the PIM generation as a property of the materialitself. The measurement curve R×A therefore represents the culminationof all these effects, and the dotted curving showing how modest low-passfiltering of the measurement data can remove some of the noisierhigh-frequency components, showing a more favorable comparison to themodeled signal.

Alternatively, FIG. 8 depicts a rusty bolt as the passiveintermodulation source, wherein a band 14 transmission and a band 17transmission are compared to a band 14-17 intermodulation envelope. FIG.8 depicts a similar process (as noted above) using a neural-networklearning algorithm. The upper chart shows a sample taken using a rustedsteel bolt. Here the band 14 transmission curve is developed using thefixed 3rd order model. The band 17 transmission below is the same data,though this time processed through a neural-network. Close tracking ofthe estimate can be seen in FIG. 8. especially with the time-averageddata of the actual measured response shown as the dotted line.

Referring now to FIG. 9, illustrated is an example schematic systemblock diagram for a method to perform passive intermodulationcancelation according to one or more embodiments. At element 900, themethod can comprise identifying a transmission port associated with asignal interference of a reception port (e.g., via the intermodulationdetection component 208). At element 902, the method can comprisedetermining an interference product associated with the signalinterference (e.g., via the analysis component 206). Based on thedetermining, the method can comprise ranking (e.g., via the rankingcomponent 312) the interference product according to a magnitude of thesignal interference, resulting in a first ranked interference signal atelement 904. Additionally, based on a condition associated with thefirst ranked interference signal being determined to have beensatisfied, the method can comprise generating (e.g., via the signalgeneration component 308), by the wireless network device, a secondranked interference signal that conforms to the first rankedinterference signal at element 906.

Referring now to FIG. 10, illustrated is a block diagram of a computingenvironment in accordance with various aspects described herein. Forexample, in some embodiments, the computer can be or be included withinthe radio repeater system disclosed in any of the previous systems 100,200, 300, and/or 400.

In order to provide additional context for various embodiments describedherein, FIG. 10 and the following discussion are intended to provide abrief, general description of a suitable computing environment 1000 inwhich the various embodiments of the embodiment described herein can beimplemented. While the embodiments have been described above in thegeneral context of computer-executable instructions that can run on oneor more computers, those skilled in the art will recognize that theembodiments can be also implemented in combination with other programmodules and/or as a combination of hardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the inventive methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

The terms “first,” “second,” “third,” and so forth, as used in theclaims, unless otherwise clear by context, is for clarity only anddoesn't otherwise indicate or imply any order in time. For instance, “afirst determination,” “a second determination,” and “a thirddetermination,” does not indicate or imply that the first determinationis to be made before the second determination, or vice versa, etc.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically include a variety of media, which caninclude computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structured dataor unstructured data.

Computer-readable storage media can include, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devicesor other tangible and/or non-transitory media which can be used to storedesired information. In this regard, the terms “tangible” or“non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and includes any information deliveryor transport media. The term “modulated data signal” or signals refersto a signal that has one or more of its characteristics set or changedin such a manner as to encode information in one or more signals. By wayof example, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media.

With reference again to FIG. 10, the example environment 1000 forimplementing various embodiments of the aspects described hereinincludes a computer 1002, the computer 1002 including a processing unit1004, a system memory 1006 and a system bus 1008. The system bus 1008couples system components including, but not limited to, the systemmemory 1006 to the processing unit 1004. The processing unit 1004 can beany of various commercially available processors. Dual microprocessorsand other multi-processor architectures can also be employed as theprocessing unit 1004.

The system bus 1008 can be any of several types of bus structure thatcan further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 1006includes ROM 1010 and RAM 1012. A basic input/output system (BIOS) canbe stored in a non-volatile memory such as ROM, erasable programmableread only memory (EPROM), EEPROM, which BIOS contains the basic routinesthat help to transfer information between elements within the computer1002, such as during startup. The RAM 1012 can also include a high-speedRAM such as static RAM for caching data.

The computer 1002 further includes an internal hard disk drive (HDD)1014 (e.g., EIDE, SATA), which internal hard disk drive 1014 can also beconfigured for external use in a suitable chassis (not shown), amagnetic floppy disk drive (FDD) 1016, (e.g., to read from or write to aremovable diskette 1018) and an optical disk drive 1020, (e.g., readinga CD-ROM disk 1022 or, to read from or write to other high capacityoptical media such as the DVD). The hard disk drive 1014, magnetic diskdrive 1016 and optical disk drive 1020 can be connected to the systembus 1008 by a hard disk drive interface 1024, a magnetic disk driveinterface 1026 and an optical drive interface 1028, respectively. Theinterface 1024 for external drive implementations includes at least oneor both of Universal Serial Bus (USB) and Institute of Electrical andElectronics Engineers (IEEE) 1394 interface technologies. Other externaldrive connection technologies are within contemplation of theembodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 1002, the drives andstorage media accommodate the storage of any data in a suitable digitalformat. Although the description of computer-readable storage mediaabove refers to a hard disk drive (HDD), a removable magnetic diskette,and a removable optical media such as a CD or DVD, it should beappreciated by those skilled in the art that other types of storagemedia which are readable by a computer, such as zip drives, magneticcassettes, flash memory cards, cartridges, and the like, can also beused in the example operating environment, and further, that any suchstorage media can contain computer-executable instructions forperforming the methods described herein.

A number of program modules can be stored in the drives and RAM 1012,including an operating system 1030, one or more application programs1032, other program modules 1034 and program data 1036. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM 1012. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

A user can enter commands and information into the computer 1002 throughone or more wired/wireless input devices, e.g., a keyboard 1038 and apointing device, such as a mouse 1040. Other input devices (not shown)can include a microphone, an infrared (IR) remote control, a joystick, agame pad, a stylus pen, touch screen or the like. These and other inputdevices are often connected to the processing unit 1004 through an inputdevice interface 1042 that can be coupled to the system bus 1008, butcan be connected by other interfaces, such as a parallel port, an IEEE1394 serial port, a game port, a universal serial bus (USB) port, an IRinterface, etc.

A monitor 1044 or other type of display device can be also connected tothe system bus 1008 via an interface, such as a video adapter 1046. Inaddition to the monitor 1044, a computer typically includes otherperipheral output devices (not shown), such as speakers, printers, etc.

The computer 1002 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 1048. The remotecomputer(s) 1048 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer1002, although, for purposes of brevity, only a memory/storage device1050 is illustrated. The logical connections depicted includewired/wireless connectivity to a local area network (LAN) 1052 and/orlarger networks, e.g., a wide area network (WAN) 1054. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 1002 can beconnected to the local network 1052 through a wired and/or wirelesscommunication network interface or adapter 1056. The adapter 1056 canfacilitate wired or wireless communication to the LAN 1052, which canalso include a wireless AP disposed thereon for communicating with thewireless adapter 1056.

When used in a WAN networking environment, the computer 1002 can includea modem 1058 or can be connected to a communications server on the WAN1054 or has other means for establishing communications over the WAN1054, such as by way of the Internet. The modem 1058, which can beinternal or external and a wired or wireless device, can be connected tothe system bus 1008 via the input device interface 1042. In a networkedenvironment, program modules depicted relative to the computer 1002 orportions thereof, can be stored in the remote memory/storage device1050. It will be appreciated that the network connections shown areexample and other means of establishing a communications link betweenthe computers can be used.

The computer 1002 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, restroom), and telephone. This can include Wireless Fidelity(Wi-Fi) and BLUETOOTH® wireless technologies. Thus, the communicationcan be a predefined structure as with a conventional network or simplyan ad hoc communication between at least two devices.

Wi-Fi can allow connection to the Internet from a couch at home, a bedin a hotel room or a conference room at work, without wires. Wi-Fi is awireless technology similar to that used in a cell phone that enablessuch devices, e.g., computers, to send and receive data indoors and out;anywhere within the range of a base station. Wi-Fi networks use radiotechnologies called IEEE 802.11 (a, b, g, n, ac, etc.) to providesecure, reliable, fast wireless connectivity. A Wi-Fi network can beused to connect computers to each other, to the Internet, and to wirednetworks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operatein the unlicensed 2.4 and 5 GHz radio bands, at an 11 Mbps (802.11a) or54 Mbps (802.11b) data rate, for example or with products that containboth bands (dual band), so the networks can provide real-worldperformance similar to the basic 10 BaseT wired Ethernet networks usedin many offices.

In an embodiment of the subject application, the computer 1002 canprovide the environment and/or setting in which one or more of thepassive intermodulation detection and cancelation systems disclosed inFIGS. 1-4 can be operated from. For instance, the virtual machinesdisclosed herein can be applications 1032 stored in hard drive 1014 andexecuted by processing unit 1004.

FIG. 11 illustrates an example embodiment 1100 of a mobile networkplatform 1110 that can implement and exploit one or more aspects of thedisclosed subject matter described herein. Generally, wireless networkplatform 1110 can include components, e.g., nodes, gateways, interfaces,servers, or disparate platforms, that facilitate both packet-switched(PS) (e.g., internet protocol (IP), frame relay, asynchronous transfermode (ATM)) and circuit-switched (CS) traffic (e.g., voice and data), aswell as control generation for networked wireless telecommunication. Asa non-limiting example, wireless network platform 1110 can be includedin telecommunications carrier networks, and can be consideredcarrier-side components as discussed elsewhere herein. Mobile networkplatform 1110 includes CS gateway node(s) 1112 which can interface CStraffic received from legacy networks like telephony network(s) 1140(e.g., public switched telephone network (PSTN), or public land mobilenetwork (PLMN)) or a signaling system #7 (SS7) network 1170. Circuitswitched gateway node(s) 1112 can authorize and authenticate traffic(e.g., voice) arising from such networks. Additionally, CS gatewaynode(s) 1112 can access mobility, or roaming, data generated through SS7network 1170; for instance, mobility data stored in a visited locationregister (VLR), which can reside in memory 1130. Moreover, CS gatewaynode(s) 1112 interfaces CS-based traffic and signaling and PS gatewaynode(s) 1118. As an example, in a 3GPP UMTS network, CS gateway node(s)1112 can be realized at least in part in gateway GPRS support node(s)(GGSN). It should be appreciated that functionality and specificoperation of CS gateway node(s) 1112, PS gateway node(s) 1118, andserving node(s) 1116, is provided and dictated by radio technology(ies)utilized by mobile network platform 1110 for telecommunication. Mobilenetwork platform 1110 can also include the MMEs, HSS/PCRFs, SGWs, andPGWs disclosed herein.

In addition to receiving and processing CS-switched traffic andsignaling, PS gateway node(s) 1118 can authorize and authenticatePS-based data sessions with served mobile devices. Data sessions caninclude traffic, or content(s), exchanged with networks external to thewireless network platform 1110, like wide area network(s) (WANs) 1150,enterprise network(s) 1170, and service network(s) 1180, which can beembodied in local area network(s) (LANs), can also be interfaced withmobile network platform 1110 through PS gateway node(s) 1118. It is tobe noted that WANs 1150 and enterprise network(s) 1160 can embody, atleast in part, a service network(s) like IP multimedia subsystem (IMS).Based on radio technology layer(s) available in technology resource(s)1117, packet-switched gateway node(s) 1118 can generate packet dataprotocol contexts when a data session is established; other datastructures that facilitate routing of packetized data also can begenerated. To that end, in an aspect, PS gateway node(s) 1118 caninclude a tunnel interface (e.g., tunnel termination gateway (TTG) in3GPP UMTS network(s) (not shown)) which can facilitate packetizedcommunication with disparate wireless network(s), such as Wi-Finetworks.

In embodiment 1100, wireless network platform 1110 also includes servingnode(s) 1116 that, based upon available radio technology layer(s) withintechnology resource(s) 1117, convey the various packetized flows of datastreams received through PS gateway node(s) 1118. It is to be noted thatfor technology resource(s) 1117 that rely primarily on CS communication,server node(s) can deliver traffic without reliance on PS gatewaynode(s) 1118; for example, server node(s) can embody at least in part amobile switching center. As an example, in a 3GPP UMTS network, servingnode(s) 1116 can be embodied in serving GPRS support node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s)1114 in wireless network platform 1110 can execute numerous applicationsthat can generate multiple disparate packetized data streams or flows,and manage (e.g., schedule, queue, format . . . ) such flows. Suchapplication(s) can include add-on features to standard services (forexample, provisioning, billing, customer support . . . ) provided bywireless network platform 1110. Data streams (e.g., content(s) that arepart of a voice call or data session) can be conveyed to PS gatewaynode(s) 1118 for authorization/authentication and initiation of a datasession, and to serving node(s) 1116 for communication thereafter. Inaddition to application server, server(s) 1114 can include utilityserver(s), a utility server can include a provisioning server, anoperations and maintenance server, a security server that can implementat least in part a certificate authority and firewalls as well as othersecurity mechanisms, and the like. In an aspect, security server(s)secure communication served through wireless network platform 1110 toensure network's operation and data integrity in addition toauthorization and authentication procedures that CS gateway node(s) 1112and PS gateway node(s) 1118 can enact. Moreover, provisioning server(s)can provision services from external network(s) like networks operatedby a disparate service provider; for instance, WAN 1150 or GlobalPositioning System (GPS) network(s) (not shown). Provisioning server(s)can also provision coverage through networks associated to wirelessnetwork platform 1110 (e.g., deployed and operated by the same serviceprovider), such as femto-cell network(s) (not shown) that enhancewireless service coverage within indoor confined spaces and offload RANresources in order to enhance subscriber service experience within ahome or business environment by way of UE 1175.

It is to be noted that server(s) 1114 can include one or more processorsconfigured to confer at least in part the functionality of macro networkplatform 1110. To that end, the one or more processor can execute codeinstructions stored in memory 1130, for example. It is should beappreciated that server(s) 1114 can include a content manager 1115,which operates in substantially the same manner as describedhereinbefore.

In example embodiment 1100, memory 1130 can store information related tooperation of wireless network platform 1110. Other operationalinformation can include provisioning information of mobile devicesserved through wireless platform network 1110, subscriber databases;application intelligence, pricing schemes, e.g., promotional rates,flat-rate programs, couponing campaigns; technical specification(s)consistent with telecommunication protocols for operation of disparateradio, or wireless, technology layers; and so forth. Memory 1130 canalso store information from at least one of telephony network(s) 1140,WAN 1150, enterprise network(s) 1160, or SS7 network 1170. In an aspect,memory 1130 can be, for example, accessed as part of a data storecomponent or as a remotely connected memory store.

In order to provide a context for the various aspects of the disclosedsubject matter, FIGS. 10 and 11, and the following discussion, areintended to provide a brief, general description of a suitableenvironment in which the various aspects of the disclosed subject mattercan be implemented. While the subject matter has been described above inthe general context of computer-executable instructions of a computerprogram that runs on a computer and/or computers, those skilled in theart will recognize that the disclosed subject matter also can beimplemented in combination with other program modules. Generally,program modules include routines, programs, components, data structures,etc. that perform particular tasks and/or implement particular abstractdata types.

Furthermore, for future SDN (software defined networks) with NFV(network function virtualization), the cancelation can be in the form ofa software module that can be implemented (or not if there is no PIMdegradation) inside the virtualized RAN module. This can scale theapproach to include PIM cancelation for PIM originating from multiplecarrier interactions.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can include both volatile andnonvolatile memory, by way of illustration, and not limitation, volatilememory (see below), non-volatile memory (see below), disk storage (seebelow), and memory storage (see below). Further, nonvolatile memory canbe included in read only memory (ROM), programmable ROM (PROM),electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), or flash memory. Volatile memory can include random accessmemory (RAM), which acts as external cache memory. By way ofillustration and not limitation, RAM is available in many forms such assynchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM),double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), SynchlinkDRAM (SLDRAM), and direct Rambus RAM (DRRAM). Additionally, thedisclosed memory components of systems or methods herein are intended tocomprise, without being limited to comprising, these and any othersuitable types of memory.

Moreover, it will be noted that the disclosed subject matter can bepracticed with other computer system configurations, includingsingle-processor or multiprocessor computer systems, mini-computingdevices, mainframe computers, as well as personal computers, hand-heldcomputing devices (e.g., PDA, phone, watch, tablet computers, netbookcomputers, . . . ), microprocessor-based or programmable consumer orindustrial electronics, field programmable gate array, graphicsprocessor, or software defined radio reconfigurable processor and thelike. The illustrated aspects can also be practiced in distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network; however, someif not all aspects of the subject disclosure can be practiced onstand-alone computers. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

The embodiments described herein can employ artificial intelligence (AI)to facilitate automating one or more features described herein. Theembodiments (e.g., in connection with passive intermodulationcancelation techniques) can employ various AI-based schemes for carryingout various embodiments thereof. Moreover, a classifier can be employedto determine a ranking or priority of the products as a function of thesignals. A classifier is a function that maps an input attribute vector,x=(x1, x2, x3, x4, . . . , xn), to a confidence that the input belongsto a class, that is, f(x)=confidence(class). Such classification canemploy a probabilistic and/or statistical-based analysis (e.g.,factoring into the analysis utilities and costs) to prognose or infer anaction that a user desires to be automatically performed. A supportvector machine (SVM) is an example of a classifier that can be employed.The SVM operates by finding a hypersurface in the space of possibleinputs, which the hypersurface attempts to split the triggering criteriafrom the non-triggering events. Intuitively, this makes theclassification correct for testing data that is near, but not identicalto training data. Other directed and undirected model classificationapproaches include, e.g., naïve Bayes, Bayesian networks, decisiontrees, neural networks, fuzzy logic models, and probabilisticclassification models providing different patterns of independence canbe employed to cancel signal interference. Classification as used hereinalso is inclusive of statistical regression that is utilized to developmodels of priority.

As will be readily appreciated, one or more of the embodiments canemploy classifiers that are explicitly trained (e.g., via a generictraining data) as well as implicitly trained (e.g., via observing UEbehavior, operator preferences, historical information, receivingextrinsic information). For example, SVMs can be configured via alearning or training phase within a classifier constructor and featureselection module. Thus, the classifier(s) can be used to automaticallylearn and perform a number of functions, including but not limited todetermining according to a predetermined criteria which signal productshould have the highest severity based on the interference and/or thecarrier itself, etc.

As used in this application, in some embodiments, the terms “component,”“system” and the like are intended to refer to, or include, acomputer-related entity or an entity related to an operational apparatuswith one or more specific functionalities, wherein the entity can beeither hardware, a combination of hardware and software, software, orsoftware in execution. As an example, a component may be, but is notlimited to being, a process running on a processor, a processor, anobject, an executable, a thread of execution, computer-executableinstructions, a program, and/or a computer. By way of illustration andnot limitation, both an application running on a server and the servercan be a component. One or more components may reside within a processand/or thread of execution and a component may be localized on onecomputer and/or distributed between two or more computers. In addition,these components can execute from various computer readable media havingvarious data structures stored thereon. The components may communicatevia local and/or remote processes such as in accordance with a signalhaving one or more data packets (e.g., data from one componentinteracting with another component in a local system, distributedsystem, and/or across a network such as the Internet with other systemsvia the signal). As another example, a component can be an apparatuswith specific functionality provided by mechanical parts operated byelectric or electronic circuitry, which is operated by a software orfirmware application executed by a processor, wherein the processor canbe internal or external to the apparatus and executes at least a part ofthe software or firmware application. As yet another example, acomponent can be an apparatus that provides specific functionalitythrough electronic components without mechanical parts, the electroniccomponents can include a processor therein to execute software orfirmware that confers at least in part the functionality of theelectronic components. While various components have been illustrated asseparate components, it will be appreciated that multiple components canbe implemented as a single component, or a single component can beimplemented as multiple components, without departing from exampleembodiments.

Further, the various embodiments can be implemented as a method,apparatus or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device or computer-readable storage/communicationsmedia. For example, computer readable storage media can include, but arenot limited to, magnetic storage devices (e.g., hard disk, floppy disk,magnetic strips), optical disks (e.g., compact disk (CD), digitalversatile disk (DVD)), smart cards, and flash memory devices (e.g.,card, stick, key drive). Of course, those skilled in the art willrecognize many modifications can be made to this configuration withoutdeparting from the scope or spirit of the various embodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or”. That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Moreover, terms such as “user equipment,” “mobile station,” “mobile,”subscriber station,” “access terminal,” “terminal,” “handset,” “mobiledevice” (and/or terms representing similar terminology) can refer to awireless device utilized by a subscriber or user of a wirelesscommunication service to receive or convey data, control, voice, video,sound, gaming or substantially any data-stream or signaling-stream. Theforegoing terms are utilized interchangeably herein and with referenceto the related drawings.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer” andthe like are employed interchangeably throughout, unless contextwarrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based, at least, on complex mathematical formalisms),which can provide simulated vision, sound recognition and so forth.

As employed herein, the term “processor” can refer to substantially anycomputing processing unit or device comprising, but not limited tocomprising, single-core processors; single-processors with softwaremultithread execution capability; multi-core processors; multi-coreprocessors with software multithread execution capability; multi-coreprocessors with hardware multithread technology; parallel platforms; andparallel platforms with distributed shared memory. Additionally, aprocessor can refer to an integrated circuit, an application specificintegrated circuit (ASIC), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), a programmable logic controller (PLC), acomplex programmable logic device (CPLD), a discrete gate or transistorlogic, discrete hardware components or any combination thereof designedto perform the functions described herein. Processors can exploitnano-scale architectures such as, but not limited to, molecular andquantum-dot based transistors, switches and gates, in order to optimizespace usage or enhance performance of user equipment. A processor canalso be implemented as a combination of computing processing units.

What has been described above includes mere examples of variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing these examples, but one of ordinary skill in the art canrecognize that many further combinations and permutations of the presentembodiments are possible. Accordingly, the embodiments disclosed and/orclaimed herein are intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the detailed description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

What is claimed is:
 1. A method, comprising: identifying, by a networkdevice, comprising a processor, of a wireless network, a transmissionport associated with a signal interference of a reception port;determining, by the network device, an interference product associatedwith the signal interference; based on the determining, ranking, by thenetwork device, the interference product according to a magnitude of thesignal interference, resulting in a first ranked interference signal;based on a condition associated with the first ranked interferencesignal being determined to have been satisfied, generating, by thenetwork device, a second ranked interference signal that conforms to thefirst ranked interference signal; in response to the ranking,generating, by the network device, a channel associated with thetransmission port and the reception port to modify a transmission signalassociated with the transmission port; based on the transmission signal,estimating, by the network device, an interference signal at thereception port, resulting in an estimated signal; and subtracting, bythe network device, the estimated signal from the second rankedinterference signal, resulting in an error signal.
 2. The method ofclaim 1, wherein the signal interference is a first signal interference,and further comprising: based on the error signal, canceling, by thenetwork device, a second signal interference associated with a secondsignal.
 3. The method of claim 2, further comprising: routing, by thenetwork device, the second signal to a canceler device associated withthe reception port, to cancel a third signal interference.
 4. The methodof claim 3, further comprising: based on the condition associated withthe first ranked interference signal being determined to have beensatisfied, estimating, by the network device, the second rankedinterference signal.
 5. The method of claim 1, further comprising: basedon the error signal, canceling, by the network device, the signalinterference associated with the reception port.
 6. The method of claim1, wherein the estimated signal is a first estimated signal, and furthercomprising: utilizing, by the network device, the error signal todetermine a second estimated signal.
 7. The method of claim 6, furthercomprising: based on the error signal and the second estimated signal,modifying, by the network device, a third estimated signal.
 8. Themethod of claim 1, wherein the generating the second ranked interferencesignal comprises generating the second ranked interference signal inaccordance with a limit on physical resource block utilization of thewireless network.
 9. A system, comprising: a processor; and a memorythat stores executable instructions that, when executed by theprocessor, facilitate performance of operations, comprising: determininga signal interference associated with a signal received by a receiverdevice; determining an interference product as a function of the signalinterference; based on the interference product, generating rank datarepresentative of a rank of the signal interference, and applying therank data to the signal interference to generate a ranked interferencesignal; in response to the applying, generating a channel associatedwith a transmission device and the receiver device to modify atransmission signal associated with the transmission device; based onthe transmission signal, estimating an interference signal at thereceiver device, resulting in an estimated signal; and subtracting theestimated signal from the ranked interference signal, resulting in anerror signal.
 10. The system of claim 9, wherein the signal is a firstsignal, wherein the signal interference is a first signal interference,wherein the estimated signal is a first estimated signal, and whereinthe operations further comprise: estimating a second signalinterference, associated with a second signal, resulting in a secondestimated signal.
 11. The system of claim 10, wherein the error signalis a first error signal, and wherein the operations further comprise:subtracting a first value associated with the second estimated signalfrom a second value associated with the ranked interference signal,resulting in a second error signal.
 12. The system of claim 11, whereinthe operations further comprise: based on the second error signal,canceling the second signal interference associated with the secondsignal.
 13. The system of claim 12, wherein the operations furthercomprise: based on the second error signal and the second estimatedsignal, modifying a third estimated signal.
 14. The system of claim 10,wherein the operations further comprise: routing the second signal to acanceler device associated with the receiver device, to cancel a thirdsignal interference.
 15. A machine-readable storage medium, comprisingexecutable instructions that, when executed by a processor, facilitateperformance of operations, comprising: receiving first signal datarelated to a transmission signal from a transmission port device to areception port device, wherein a first signal interference associatedwith the transmission signal is determined to be present at thereception port device; based on determining an interference productassociated with the first signal interference, ranking the interferenceproduct, resulting in rank data; based on the rank data, generating aranked interference signal; generating a second signal interference anda channel associated with the transmission port device and the receptionport device to modify the transmission signal associated with thetransmission port device; based on the transmission signal, estimatingan interference signal at the reception port device, resulting in anestimated signal; and subtracting the estimated signal from the rankedinterference signal, resulting in an error signal.
 16. Themachine-readable storage medium of claim 15, wherein the transmissionsignal is a first transmission signal, and wherein the operationsfurther comprise: generating the channel associated with thetransmission port device and the reception port device to modify asecond transmission signal from the transmission port device to thereception port device.
 17. The machine-readable storage medium of claim16, wherein the operations further comprise: based on the secondtransmission signal, estimating the second signal interferenceexperienced by the reception port device do to the second transmissionsignal.
 18. The machine-readable storage medium of claim 17, wherein theoperations further comprise: reducing a first value associated with theranked interference signal by a second value associated with the secondsignal interference, resulting in a third value associated with theerror signal.
 19. The machine-readable storage medium of claim 18,wherein the operations further comprise: utilizing the third value todetermine a third signal interference of a third signal.
 20. Themachine-readable storage medium of claim 15, wherein the ranking theinterference product comprises ranking the interference product inaccordance with a constraint on utilization of a physical resource blockof a wireless network.